Purchasing optimization system

ABSTRACT

An automated system ( 100 ) and method for optimizing purchasing activity from the perspective of the buyer. The purchasing activity features and resources are defined using purchasing system data. The suppliers and deliverables are then mapped to one or more frames within the Market Value Matrix™ System for the Buyer. The system then identifies the mix of features and resources that maximize the expected value from purchasing activity from the different Buyer frames.

CROSS REFERENCE TO RELATED APPLICATIONS AND PATENTS

The subject matter of this application is related to the subject matter of application Ser. No. 09/295,337 filed Apr. 21, 1999 (now abandoned), Ser. No. 09/421,553 filed Oct. 20, 1999 (now abandoned), Ser. No. 09/775,561 filed Feb. 5, 2001 (now abandoned), application Ser. No. 09/678,109 filed Oct. 4, 2000, application Ser. No. 09/938,555 filed Aug. 27, 2001 (now abandoned), application Ser. No. 09/994,720 filed Nov. 28, 2001, application Ser. No. 09/994,739 filed Nov. 28, 2001, application Ser. No. 10/046,316 filed Jan. 16, 2002, application Ser. No. 10/012,375 filed Dec. 12, 2001, application Ser. No. 10/025,794 filed Dec. 26, 2001, application Ser. No. 10/036,522 filed Jan. 7, 2002, application Ser. No. 10/124,240 filed Apr. 18, 2002, U.S. Pat. No. 5,615,109 “Method of and System for Generating Feasible, Profit Maximizing Requisition Sets and U.S. Pat. No. 6,321,205 “Method of and System for Modeling and Analyzing Business Improvement Programs” by Jeff S. Eder, the disclosure of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to a computer based method of and system for optimizing purchasing activity in a manner that maximizes expected returns while minimizing risk for the enterprise or multi-enterprise organization that is the buyer.

The spectacular splash caused by the Enron bankruptcy has made the public aware of something that professionals have known for a long time—the general ledger does not capture many of the factors that are relevant to accurately assessing corporate financial performance. In fact, the Enron bankruptcy was just the tip of the iceberg as a major financial news publication recently reported that “an examination of the 673 largest bankruptcies of public corporations since 1996 shows that in 54% of cases, no warnings were issued in the audit reports”. By now, the deficiencies in the general ledger system which include a failure to include intangible assets; a failure to include real option values; a failure to include data about most risks; and a failure to include data about the impact of most external factors are well known by most investors.

Unfortunately, the deficiencies in the general ledger have been replicated by all known systems for financial management, operations management, risk management and purchasing. These “narrow systems” are nominally supposed to help a business “manage” a subset of its operation. The specific deficiencies in narrow systems include:

-   -   1) failure to analyze the impact of change in the narrow part of         the operation being analyzed/managed on related parts of the         operation;     -   2) failure to consider soft (aka intangible) assets—this is         closely related to the first deficiency because soft assets are         generally more inter-related than many hard assets;     -   3) failure to analyze the impact of change on more than segment         of value—most systems only focus on the current operation;     -   4) failure to analyze the impact of all relevant external         factors on changes being suggested—many assume they can not be         managed which is often not the case;     -   5) failure to analyze the impact of change on enterprise         risk—most systems “handle risk” by adjusting the discount rate         rather than analyzing the impact on all classes of enterprise         risk as a result, actions that reduce enterprise risk can be         ignored as they do not impact the valuation placed on the         activity which reduces risk; and     -   6) a focus on “efficiency” rather than value impact; and     -   7) the use of outdated value metrics.         Taken together these limitations severely restrict the         usefulness of the analyses and management direction provided by         these narrow systems.

The seven limitations represent two general types of shortcomings in narrow systems. The first five shortcomings can be summarized in to a general statement that narrow systems do not have the contextual background they need for analyzing and managing the part of the enterprise they are designed to support. The implicit assumption in these systems is that the portion of the enterprise they are supporting is independent from the rest of the enterprise and the external environment. Because this is almost never true, narrow systems are generally operating in a manner that is out of touch with reality. Changing even the narrowest slice of the enterprise will generally have an impact on other tangible assets, other intangible assets, other segments of value and enterprise risk. Ignoring these impacts severely diminishes the value of the analysis and recommendations provided by narrow systems. Ignoring these impacts is the equivalent of a doctor providing a drug to optimize kidney performance without considering the impact on other organs and the overall health of the patient. The good news is the kidney is working great, the bad news is the patient died. Perhaps this is the underlying reason that some studies suggest American industry has wasted over $400 Billion on management systems that are not providing any payback.

At this point it is important to distinguish the strategic business context described above with the “administrative context” that is starting to appear in offerings from narrow system vendors. Some are using portals and similar applications to aggregate and display information from different systems to give the users a more complete background or context in which to make their decisions. The implicit message in these systems is “we think this other information might be relevant to your decision but we don't know how important it is so we will display it and let you figure it out”. A system that was able to sort through the different systems and consistently define the proper context for decision making would obviously be an enormous improvement. Other narrow system vendors are more focused on completing transactions in an automated fashion. Doing this requires among other things: the detailed procedure for completing the transaction (i.e. where the money goes, how soon it has to be paid, etc.), the details of the specific transaction—how much of which product should ship, etc. and recent transaction history. Unlike the strategic context information which has not been available from any system before the development of the methods and systems described in the cross-referenced patent applications, the administrative context information is readily available and the major reason for aggregating it in a server or layer is to speed processing rather than provide any new capabilities. Indeed, in the cross-referenced patent applications the readily available administrative context information is included with technical information, market information and the strategic business context information in the layers propagated by the systems described therein.

The second general type of shortcoming of narrow systems is a product of the final two limitations listed above. These limitations can be summarized as a reliance on metrics instead of direct measures of market requirements for value creation. The goal of all narrow applications is to improve market value for the firms that use them. Because of limitations in data availability, a historical shortage of processing power and the lack of robust models for the full spectrum of value creation in the modern enterprise, metrics such as accounting profit, EVA®, the Balanced Scorecard and CFROI®) have been developed to give managers a shorthand method for evaluating the “value impact” of their decisions. Unfortunately, these metrics have an uncertain and highly variable relationship with actual market performance. For example, the value relevance of “accounting profit” has been declining steadily for 30 years. The declining relevance of accounting profits has a negative impact on other metrics which are generally some variant of accounting profit. Fortunately, the advances in data availability, processing power and the robust systems and methods for evaluating the full spectrum of value creation detailed in the cross-referenced patent applications enables the direct measurement of the requirements for market value creation. This eliminates the need for metrics which may or may not relate to market value creation.

The “efficient frontier” for each enterprise (as detailed in the cross-referenced patent application Ser. Nos. 09/994,720, 09/994,739, 10,046,316 and 10/124,240) provides a concise way to overcome the seven specific shortcomings of existing narrow systems. A change that moves the company closer to its efficient frontier (when the frontier is defined using the methods and systems detailed in the cross-referenced application Ser. Nos. 09/994,720, 09/994,739, 10,046,316 and 10/124,240) would be one that on balance provided a benefit to the organization when all the relevant context and market requirements are properly considered in the analysis.

All known purchasing systems suffer from the limitations described above. In many cases these shortcomings are compounded the fact that these systems also lack the ability to properly analyze the volume purchase discounts many suppliers offer. Because the great bulk of the cost of many manufactured items consists of purchased parts, the absence of a system that can effectively analyze the offerings from different vendors is a major problem for most companies. The severity of this problem has been exacerbated recently as firms seek ways to more effectively collaborate with their “partners” and suppliers rather than just simply complete spot transactions. One manifestation of this increased collaboration has been a willingness to share the risks as well as the rewards associated with new endeavors. Management systems that bury risk measures in the discount rate are obviously of little help when determining the best way to share risks.

In light of the preceding discussion, it is clear that it would be desirable to provide purchasing managers with the ability to optimize purchasing activity after considering the relevant context factors, market value factors and volume purchase discount schedules. Ideally, this system would be capable of optimizing purchasing activity for companies that are closely collaborating with their suppliers.

SUMMARY OF THE INVENTION

It is a general object of the present invention to provide a novel and useful system that optimizes purchasing activity in a way that maximize expected value while minimizing risk for the enterprise or multi-enterprise organization that is the buyer. This new system overcomes the limitations and drawbacks of the prior art that were described previously. The system of the present invention is the first known system with the ability to optimize purchasing activity from the perspective of an enterprise, a multi-enterprise organization and/or a collaborative multi-enterprise operation. The collaborative multi-enterprise operation is analyzed using a multi-enterprise organization perspective. These different perspectives will hereinafter be referred to as frames.

Before going further, we need to define the term's feature, buyer and economic benefit. Features encapsulate all the different options the purchasing manager has for acquiring the materials he is expected to deliver (the deliverables). For example, the purchasing manager could buy a one month supply of item A for a certain price or he could buy a 3 month supply of item A for a different, presumably lower, price. The purchasing manager may also have the option of substituting item A1 for item A at a different price. For our purposes, the buyer will be the enterprise or multi-enterprise organization that is expected to the first consumer of the deliverables from the purchasing activity. An economic benefit will be defined as improving the value or reducing the risk associated with one or more cells within the Market Value Matrix™ System, the matrix of value and/or the matrix of risk (hereinafter, the Market Value Matrix™) for the buyer. In some cases, the buyer may not be the enterprise or organization operating the purchasing system. It should also be noted at this point that the system of the present invention can be used to optimize purchasing activity from other frames in addition to the frames described previously.

Analyzing and optimizing purchasing activity from the buyer's frame requires a complete understanding of the strategic business context and the market requirements for the buyer. A purchasing optimization analysis that incorporates the context and market information required for meaningful analysis can be completed using three different approaches.

The first approach for completing the optimization analyses involves mapping the purchasing activity and suppliers to the buyer's Market Value Matrix™ System where the optimal mix of features for purchasing activity—and all other activities—can be determined. The mapping occurs in two steps. The first step requires mapping the suppliers and deliverables to cells within the frame being used within the buyer's Market Value Matrix™ System. The first mapping step can be completed by the user (20) or it can be completed in an automated fashion if the data from the purchasing system database (30) is tagged with xsd and/or xml information that identifies the cells where the purchasing activity will have an impact. The second mapping step is generally completed in an automated fashion as the specific value drivers within each cell that would be impacted by the purchasing activity are identified.

The second approach for completing the analyses involves providing the context and market requirement data required for analysis via an operating system, middleware or web services layer. In this mode, the buyer's Market Value Matrix™ System propagates a layer containing the required information for each frame being utilized in the analysis. The novel system of the present invention then extracts the required information from the proper frame within the layer and completes the optimization calculations. The optimized feature set is then communicated back to the buyer's Market Value Matrix™ System for inclusion in the most current model.

The third approach for completing the optimization is a cross between the first two methods. In this mode, the purchasing activity and suppliers are mapped to the proper frame within the buyer's Market Value Matrix™ System as required to identify the relevant context and market information. The relevant information is then extracted and the optimization calculations are then completed by the purchasing system. The optimized feature set is then communicated back to the buyer's Market Value Matrix™ System for inclusion in the most current model.

In the preferred embodiment of the present invention, the second approach described above will be used to complete the calculations. More specifically, the system of the present invention will extract the required context and market information from the buyer's Market Value Matrix™ System via a Knowledge Layer and use this information to complete the requisite calculations.

The same three approaches can be used to complete analyses for asset, process, project and risk management optimizations. For example, cross-referenced application Ser. No. 10/012,375 filed Dec. 12, 2001 describes a project optimization system that uses the third approach described above, cross-referenced application Ser. No. 10/025,794 filed Dec. 26, 2001 describes a process (and asset) optimization system that uses the third approach described above and cross-referenced application Ser. No. 10/036,522 filed Jan. 7, 2002 describes a risk optimization system that uses the third approach described above. While the third approach was the preferred embodiment for those applications, it should be understood that the other two approaches could be used to complete each of the different types of optimization analyses to the same effect. These same three general methods can also be used to enable financial service providers to provide capital, evaluate creditworthiness, transfer risk, evaluate potential transactions (like acquisitions) and price securities for an enterprise or multi-enterprise organization.

In short, the system of the present invention is a specific embodiment of a general method/system for analyzing, managing and optimizing any subset of an enterprise or multi-enterprise organization by using information from the Market Value Matrix™ System. The subset can include services provided by external suppliers. The elements of this general method/system are:

-   -   1. A method/system for representing the subset of the enterprise         including its resources (inputs), deliverables (outputs) and         features;     -   2. A method/system for creating a map between the resources and         deliverables of the subset and the specified frame within the         Market Value Matrix™ System of the enterprise or         multi-enterprise organization;     -   3. A method/system for using the map from the prior step to make         the relevant context and market requirement information from the         Market Value Matrix™ System available for use in analysis; and     -   4. A method/system for optimizing the features for the subset         and frame being analyzed given the resources, deliverables,         market requirements and context.

Under this general method/system, the frame is used to define the portion of the overall context and market requirements that are considered in the analysis. This feature of the method/system gives corporations complete control over how their finance and operation management systems analyze their operations. For example, if a corporation decided that it did not want the real option segment of value included in their analyses, then it could define a frame that excluded this segment of value for all analyses. This is in sharp contrast to the existing narrow systems that have the approach they have chosen “hard wired” in to the software.

Another benefit of the approach taken in the system of the present invention is that the automated extraction, aggregation and analysis of data from a variety of existing computer-based systems significantly increases the scale and scope of the analyses that can be completed by users without a significant background in finance. To facilitate its use as a tool for improving the value of purchasing activities, the system of the present invention produces reports in formats that are graphical and highly intuitive. This capability gives purchasing managers the tools they need to dramatically improve the long-term financial performance of the purchasing activity.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, features and advantages of the present invention will be more readily apparent from the following description of the preferred embodiment of the invention in which:

FIG. 1 is a block diagram showing the major processing steps of the present invention;

FIG. 2 is a diagram showing the files or tables in the application database of the present invention that are utilized for data storage and retrieval during the processing in the system for optimizing purchasing;

FIG. 3 is a block diagram of an implementation of the present invention;

FIG. 4 is a diagram showing the data windows that are used for receiving information from and transmitting information to the user (20) during system processing;

FIG. 5A and FIG. 5B are block diagrams showing the sequence of steps in the present invention used for extracting, aggregating and storing information utilized in system processing from: user input, the purchasing system database, optionally, the simulation program database; the Internet; and the buyer's Market Value Matrix™ System database;

FIG. 6A and FIG. 6B are block diagrams showing the sequence of steps in the present invention that are utilized in identifying the purchasing activity features that maximizes expected value while minimizing risk for the enterprise or multi-enterprise organization that is the buyer;

FIG. 7 is a block diagram showing the sequence of steps in the present invention used for completing analyses, communicating purchasing activity to other systems and displaying, selecting and printing management reports; and

FIG. 8 is a sample report showing the efficient frontier for Organization XYZ, the current position of XYZ relative to the efficient frontier and the forecast of the new position of XYZ relative to the efficient frontier after the purchasing activity is optimized.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 provides an overview of the processing completed by the innovative system for purchasing optimization. In accordance with the present invention, an automated method of and system (100) for optimizing the risk and return from purchasing activity is provided. Processing starts in this system (100) with a block of software (200) that extracts, aggregates and stores the data and user input required for completing the analysis. This information is extracted via a network (25) from a purchasing system database (30), optionally, a simulation program database (35), the Internet (40) and a buyer's Market Value Matrix™ System database (44) via a Knowledge Layer (45). These information extractions and aggregations are guided by a user (20) through interaction with a user-interface portion of the application software (900) that mediates the display and transmission of all information to the user (20) from the system (100) as well as the receipt of information into the system (100) from the user (20) using a variety of data windows tailored to the specific information being requested or displayed in a manner that is well known. While only one database of each type (30, 35 and 44) is shown in FIG. 1, it is to be understood that the system (100) can extract data from multiple databases of each type via the network (25).

All extracted information concerning the purchasing activity is stored in a file or table (hereinafter, table) within an application database (50) as shown in FIG. 2. The application database (50) contains tables for storing user input, extracted information and system calculations including a system settings table (140), a metadata mapping table (141), a conversion rules table (142), a frame definition table (143), a purchasing system database table (144), a reports table (145), an analysis definition table (146), an operating factors table (147), a simulation program table (148), a bot date table (149), a buyer's Value Matrix™ System table (150), a purchasing activity value table (151), a external factor forecast table (152), a feature option value table (153), a sensitivity analysis table (154) and a reports table (155). The application database (50) can optionally exist as a datamart, data warehouse, departmental warehouse, a virtual repository or storage area network. The system of the present invention has the ability to accept and store supplemental or primary data directly from user input, a data warehouse or other electronic files in addition to receiving data from the databases described previously. The system of the present invention also has the ability to complete the necessary calculations without receiving data from one or more of the specified databases. However, in the preferred embodiment all required information is obtained from the specified databases (30, 35 and 44) and the Internet (40).

As shown in FIG. 3, the preferred embodiment of the present invention is a computer system (100) illustratively comprised of a client personal computer (110) connected to an application server personal computer (120) via a network (25). The application server personal computer (120) is in turn connected via the network (25) to a database-server personal computer (130).

The database-server personal computer (130) has, a hard drive (131) for storage of the purchasing system database (30), optionally, the simulation program database (35), a keyboard (132), a CRT display (133), a communications bus (134) and a read/write random access memory (135), a mouse (136), a CPU (137), and a printer (138).

The application-server personal computer (120) has a hard drive (121) for storage of the application database (50) and the majority of the application software (200, 300 and 400) of the present invention, a keyboard (122), a CRT display (123), a communications bus (124), and a read/write random access memory (125), a mouse (126), a CPU (127), and a printer (128). While only one client personal computer is shown in FIG. 3, it is to be understood that the application-server personal computer (120) can be networked to fifty or more client personal computers (110) via the network (25). The application-server personal computer (120) can also be networked to fifty or more server, personal computers (130) via the network (25). It is to be understood that the diagram of FIG. 3 is merely illustrative of one embodiment of the present invention. For example the system could be housed on one or two computer or it could be distributed to more than 3 computers.

The client personal computer (110) has a hard drive (111) for storage of a client database (49) and the user-interface portion of the application software (900), a keyboard (112), a CRT display (113), a communication bus (114), a read/write random access memory (115), a mouse (116), a CPU (117), a printer (118) and a modem (119).

The application software (200, 300 and 400) controls the performance of the central processing unit (127) as it completes the calculations required for purchasing activity optimization. In the embodiment illustrated herein, the application software program (200, 300 and 400) is written in Java. The application software (200, 300 and 400) also uses Structured Query Language (SQL) for extracting data from other databases (30, 35 and 44) and then storing the data in the application database (50) or for receiving input from the user (20) and storing it in the client database (49). The other databases contain purchasing system database (30), simulations of the impact of alternative suppliers and parts (35) and the elements of value, external factors and risks of the buyer (44). The user (20) provides the information to the application software as required to determine which data need to be extracted and transferred from the database-server hard drive (131) via the network (25) to the application-server computer hard drive (121) by interacting with user-interface portion of the application software (900). The extracted information is combined with input received from the keyboard (113) or mouse (116) in response to prompts from the user-interface portion of the application software (900) before processing is completed.

User input is initially saved to the client database (49) before being transmitted to the communication bus (125) and on to the hard drive (122) of the application-server computer via the network (25). Following the program instructions of the application software, the central processing unit (127) accesses the extracted data and user input by retrieving it from the hard drive (122) using the random access memory (121) as computation workspace in a manner that is well known.

The computers (110, 120 and 130) shown in FIG. 3 illustratively are personal computers or any of the more powerful computers or workstations that are widely available. Typical memory configurations for client personal computers (110) used with the present invention should include at least 128 megabytes of semiconductor random access memory (115) and at least a 2-gigabyte hard drive (111). Typical memory configurations for the application-server personal computer (120) used with the present invention should include at least 256 megabytes of semiconductor random access memory (125) and at least a 250 gigabyte hard drive (121). Typical memory configurations for the database-server personal computer (130) used with the present invention should include at least 1024 megabytes of semiconductor random access memory (135) and at least a 500 gigabyte hard drive (131).

Using the system described above, the risk and return of the purchasing activity being analyzed will be optimized from the perspective of the buyer. Optimizing the risk and return of a purchasing activity as outlined previously is completed in three distinct stages. The first stage of processing (block 200 from FIG. 1) extracts, aggregates and stores the data from user input, internal databases (30, 35 or 44) and the internet (40) as shown in FIG. 5A and FIG. 5B. The second stage of processing (block 300 from FIG. 1) analyzes the extracted data and determines the mix of purchasing activity features and feature options that maximizes purchasing activity returns while minimizing purchasing activity risk as shown in FIG. 6. The third and final stage of processing (block 400 from FIG. 1) displays the results of the prior calculations, completes special analyses, communicates with other systems and displays detailed graphical reports and optionally prints them as shown in FIG. 8.

Data Extraction and Storage

The flow diagrams in FIG. 5A and FIG. 5B detail the processing that is completed by the portion of the application software (200) that extracts, aggregates and stores the information required for system operation from: a purchasing system database (30), optionally, a simulation program database (35), the Internet (40) and a buyer's Market Value Matrix™ System database (44) via a Knowledge Layer (45) and the user (20). A brief overview of the different databases will be presented before reviewing each step of processing completed by this portion (200) of the application software.

Purchasing systems typically have the ability to not only track historical transactions but to forecast future performance. For manufacturing firms these systems are used to monitor, coordinate, track and plan the acquisition of materials. These systems will generally maintain detailed records concerning the performance of the different vendors that supply materials to the firm including the information shown in Table 1. TABLE 1 Purchasing System - Vendor Information 1. Vendor Name 2. Vendor Number 3. Commodity Code(s) 4. Year to date dollar volume 5. Historical dollar volume 6. Percentage of deliveries rejected by QC 7. Percentage of deliveries accepted out of specification 8. Compliance with ISO 9000 9. Actual lead time required for purchases 10. Terms and conditions for purchases 11. Average Delivery Quantity Variance 12. Average Delivery Date Variance 13. EDI* vendor - Yes or No *EDI = Electronic Data Interchange

These systems also have information about current and planned orders for parts and materials including the information shown in Table 2. TABLE 2 Purchasing System - Order Information 1. Order Number 2. Vendor Name 3. Vendor Number 4. Commodity Code(s) 5. Part Number 6. Order Quantity 7. Date of Order 8. Order Due Date 9. Order Costs 10. Order Payment Terms 11. Order Shipping Method 12. Volume Purchase Discount - Yes or No Purchasing systems similar to the one described above may also be useful by distributors for use in monitoring the flow of products from a manufacturer. The system of the present invention is capable of processing data related to purchasing activity if it resides in more than one database or is produced by more than one system. The extraction, conversion and storage of the distributed data could be guided by the user (20) during system setting or the system of the present invention could identify the required systems and data in an automated fashion if the proper xsd and xml tagging is in place.

Simulation programs such as MatLab, Simulink, SPICE, etc. can optionally be used to generate performance data for changes in deliverables and suppliers by forecasting product or process performance using a new set of resources and/or features. The information regarding deliverable design and operating performance is combined with external factor price information downloaded from web sites and/or databases on the internet (40) as required to support risk and return management for the purchasing activity being analyzed. The information on external factor prices will include both current prices and future prices.

The buyer's Market Value Matrix™ System database (44) for an enterprise contains the matrix of market value and related statistics generated by the system described in the cross referenced patent application Ser No. 09/994,720 dated Nov. 28, 2001, Ser. No. 09/994,739 dated Nov. 28, 2001, Ser. No. 10,046,316 dated Jan. 16, 2002 and Ser. No. 10/124,240 dated Apr. 18, 2002.

System processing of the information from the different databases (30, 35 and 44) and the Internet (40) described above starts in a block 201, FIG. 5A, which immediately passes processing to a software block 202. The software in block 202 prompts the user (20) via the system settings data window (901) to provide system settings information. The system settings information entered by the user (20) is transmitted via the network (25) back to the application server (120) where it is stored in the system settings table (140) in the application database (50) in a manner that is well known. The specific inputs the user (20) is asked to provide at this point in processing are shown in Table 3. TABLE 3 1. Buyer 2. Time period for analysis 3. Mode of operation (continuous or batch) 4. Purchase volume discount analysis (yes, no or exclusive) 5. Metadata standard 6. Location of purchasing system database and metadata (optional) 7. Location of simulation system databases and metadata (optional) 8. Location of external database and metadata (optional) 9. Scenario (combined normal, extreme is default) 10. Location of account structure 11. Base currency 12. Risk free cost of capital 13. Risk adjusted cost of capital 14. Management report types (text, graphic, both) 15. Default reports 16. Default missing data procedure 17. Maximum time to wait for user input 18. Maximum number of generations to process without improving fitness

The specification of the location and metadata information for the purchasing system database, simulation database and external database are optional because that information may have been included in the xsd and/or xml information attached to each system and data element. If this is the case, then the software in this block would be able to locate the required data without the user (20) having to specify its metadata standard and location. The Knowledge Layer (45) contains the metadata information for the buyers' Value Matrix™ System database (44) so the user (20) is not required to specify that information in the preferred embodiment. However, that information could be provided here. The administrative portion of the Knowledge Layer (45) would be expected to contain the volume purchase discount schedules, item inventories and item requirements (note “item” and “deliverable” are used interchangeably) however, in the preferred embodiment this information is obtained from the purchasing system. In any event, after the storage of system settings data is complete, processing advances to a software block 203.

The software in block 203 prompts the user (20) via the metadata and conversion rules window (902) to map all purchasing resources and deliverables from the purchasing system database (30) and optionally, a simulation program database (35) using the metadata standard specified by the user (20) to the buyer's Market Value Matrix™ System database (44) which has a standardized format as described in cross-referenced patent application Ser. No. 10/124,240 dated Apr. 18, 2002. The metadata mapping at this stage may take the form of simply confirming the metadata mapping information extracted from the purchasing system database (30). The metadata mapping specifications are saved in the metadata mapping table (141). As part of the metadata mapping process, any purchasing system database fields that are not mapped to the Market Value Matrix™ System database (44) are defined by the user (20) as non-relevant attributes. This information is also saved in the metadata mapping table (141). After all field maps have been stored in the metadata mapping table (141), the software in block 203 prompts the user (20) via the metadata and conversion rules window (902) to optionally provide conversion rules for each metadata field for each data source. Conversion rules will include information regarding currency conversions and conversion for units of measure that may be required to consistently analyze the data. The inputs from the user (20) regarding conversion rules are stored in the conversion rules table (142) in the application database (50). After conversion rules have been stored for all fields from every data source, then processing advances to a software block 204.

The software in block 204 checks the system settings table (140) in the application database (50) to determine if the current calculation is a new calculation or a comparison to a prior calculation. If the calculation is a comparison to a prior calculation, then processing advances to a software block 208. Alternatively, if the calculation is not a comparison to a prior calculation, then processing advances to a software block 206.

The software in block 206 prompts the user (20) via the frame selection window (903) to select frames for analysis. The frames available for analysis are those defined in the buyer's Value Matrix System and made available to the system of the present invention via the Knowledge Layer (45). The frame selection screen provides a brief description of the frame, the frame time span and the detailed definition of the frame. The user (20) can also define subsets of the available frames for analysis. The specification of selected frames and any newly defined frames are stored in the frame definition table (143) in the application database (50) before processing advances to a software block 208.

The software in block 208 checks the bot date table (149) and deactivates any purchasing system data bots with creation dates before the current system date and retrieves information from the system settings table (140), metadata mapping table (141), the conversion rules table (142) and the frame definition table (143). The software in block 208 then initializes data bots for each field in the metadata mapping table (141) that mapped to the purchasing system database (30). Bots are independent components of the application that have specific tasks to perform. In the case of data acquisition bots, their tasks are to extract and convert data from a specified source and then store it in a specified location. Each data bot initialized by software block 208 will store its data in the purchasing system table (145). Every purchasing system data bot contains the information shown in Table 4. TABLE 4 1. Unique ID number (based on date, hour, minute, second of creation) 2. The data source location 3. Mapping information 4. Timing of extraction 5. buyer 6. Process 7. Frame 8. Conversion rules (if any) 9. Storage location (to allow for tracking of source and destination events) 10. Creation date (date, hour, minute, second)

After the software in block 208 initializes the bots for every mapped field within the purchasing system database (30) by frame, the bots extract and convert data in accordance with their preprogrammed instructions. After the extracted and converted data is stored in the purchasing system database table (144), processing advances to a software block 222.

The software in block 222 checks the bot date table (149) and deactivates any Market Value Matrix™ data bots with creation dates before the current system date and retrieves information from the system settings table (140), metadata mapping table (141), the conversion rules table (142) and the frame definition table (143). The software in block 222 then initializes data bots for retrieving the relevant portion of the buyer's Market Value Matrix database for each frame. Bots are independent components of the application that have specific tasks to perform. In the case of Market Value Matrix™ data bots, their tasks are to extract and convert data detailing the matrix of market value for the specified frame from the Knowledge Layer and store the information in a specified location. Each data bot initialized by software block 222 will store its data in the buyer's Value Matrix™ System table (150). Every buyer Market Value Matrix™ data bot contains the information shown in Table 5. TABLE 5 1. Unique ID number (based on date, hour, minute, second of creation) 2. Mapping information 3. Timing of extraction 4. Buyer 5. Frame 6. Conversion rules (if any) 7. Storage location (to allow for tracking of source and destination events) 8. Creation date (date, hour, minute, second)

After the software in block 222 initializes the bots they extract and convert data in accordance with their preprogrammed instructions by frame. After the extracted and converted data is stored in the buyer's Value Matrix™ System table (150) by frame, processing advances to a software block 223.

The software in block 223 checks the system settings table (140) to determine if simulation program data is being used in the purchasing activity analysis. If simulation program data are being used, then processing advances to a software block 224. Alternatively, if simulation program data are not being used, then processing advances to a software block 225.

The software in block 224 checks the bot date table (149) and deactivates any simulation program data bots with creation dates before the current system date and retrieves information from the system settings table (140), metadata mapping table (141), the conversion rules table (142) and the frame definition table (143). The software in block 224 then initializes data bots by frame for each field in the metadata mapping table (141) that mapped to a field in the simulation programs database (35). Bots are independent components of the application that have specific tasks to perform. In the case of data bots, their tasks are to extract and convert data from a specified source and then store it in a specified location. Each data bot initialized by software block 224 will store its data in the simulation programs table (148). Every simulation program data bot contains the information shown in Table 6. TABLE 6 1. Unique ID number (based on date, hour, minute, second of creation) 2. The data source location 3. Mapping information 4. Timing of extraction 5. Buyer 6. Frame 7. Simulation resource and/or deliverable 8. Conversion rules (if any) 9. Storage location (to allow for tracking of source and destination events) 10. Creation date (date, hour, minute, second)

After the software in block 224 initializes the bots for every mapped result within the simulation programs database (35) by frame, the bots extract and convert data in accordance with their preprogrammed instructions. After the extracted and converted data is stored in the simulation program table (148), processing advances to a software block 225.

The software in block 225 checks the system settings table (140) to determine if any data from external databases is being used in the purchasing activity analysis. If data from external databases are being used, then processing advances to a software block 227. Alternatively, if simulation program data are not being used, then processing advances to a software block 232.

The software in block 227 checks the bot date table (149) and deactivates any external factor price data bots with creation dates before the current system date and retrieves information from the system settings table (140), metadata mapping table (141), the conversion rules table (142) and the frame definition table (143). The software in block 227 then initializes data bots by external factor for each field in the metadata mapping table (141) that mapped to an external factor price on the Internet (40). Bots are independent components of the application that have specific tasks to perform. In the case of data bots, their tasks are to extract and convert data from a specified source for the time period and then store it in a specified location. Each data bot initialized by software block 227 will store the data it retrieves in the external factor price table (150). Every external factor price data bot contains the information shown in Table 7. TABLE 7 1. Unique ID number (based on date, hour, minute, second of creation) 2. The data source location 3. Mapping information 4. Timing of extraction 5. Buyer 6. Frame 7. External factor 8. Time period(s) 9. Conversion rules (if any) 10. Storage location (to allow for tracking of source and destination events) 11. Creation date (date, hour, minute, second)

After the software in block 227 initializes the bots for every mapped external factor on the Internet (40), the bots extract and convert data in accordance with their preprogrammed instructions. After the extracted and converted data is stored in the external factor forecast table (152), processing advances to a software block 232.

The software in block 232 compares the data in the purchasing system database table (144), the simulation program table (148), the buyer's Value Matrix™ system table (150) and the external factor forecast table (152) to determine if there any periods where required data is missing. If data is missing, then processing advances to a software block 234. Alternatively, if the required data is present for every time period, then processing advances to a software block 302.

The software in block 234 prompts the user (20) via the missing purchase data window (904) to input the missing data displayed on the window. The new information supplied by the user (20) is stored in the appropriate table before processing advances to software block 302.

Analysis

The flow diagrams in FIG. 6A and FIG. 6B detail the processing that is completed by the portion of the application software (300) that determines the mix of purchasing features and options that maximize value while minimizing risk for the buyer and for other specified frames. This portion of the application software (300) also evaluates the sensitivity of the optimal solution to changing external factor and/or feature prices. The data being analyzed is normalized before processing begins.

Processing in this portion of the application begins in software block 302. The software in block 302 checks the system settings table (140) in the application database (50) to determine if the current calculation includes volume purchase discounts. If the purchasing activity that is being optimized includes volume purchase discounts, then processing advances to a software block 352. Alternatively, if the purchasing activity does not include volume purchase discounts, then processing advances to a software block 303.

The software in block 303 identifies the frames being analyzed using data from the frame definition table (143) and then uses the information from the metadata mapping table (141) to identify the purchasing activity data that needs to be retrieved from the purchasing system database table (144) for the frames being analyzed. After retrieving the required purchasing data which includes the deliverable requirements for the time period, the current orders, items (deliverables) by supplier (resources) and pricing information, processing advances to a software block 304. The software in block 304 retrieves the metadata mapping table (141) data as required to identify and retrieve Market Value Matrix™ data via the Knowledge Layer regarding the specific value drivers that are linked to purchased resources, deliverables and features for the selected frames before processing advances to a software block 305. The software in block 305 retrieves the external factor prices for the purchasing activity being analyzed from the external factor forecast table (152) before processing advances to a software block 307.

The software in block 307 checks the system settings table (140) to determine if simulation program data is being used in the purchasing activity analysis. If simulation program data is being used, then processing advances to a software block 308. Alternatively, if simulation program data is not being used, then processing advances to a software block 309. The software in block 308 retrieves the feature, resource and deliverable data for the purchasing activity being analyzed from the simulation program table (148) before processing advances to software block 309.

The software in block 309 checks the bot date table (149) and deactivates any optimization bots with creation dates before the current system date and uses the previously retrieved information (from the system settings table (140), metadata mapping table (141), the conversion rules table (142), the frame definition table (143), the purchasing system database table (144), optionally, the simulation program table (148), the buyer's Value Matrix™ System table (150) and the external factor forecast table (152). Bots are independent components of the application that have specific tasks to perform. In the case of optimization bots, their primary task is to determine the optimal mix of purchasing activity features by frame. The optimal mix is the mix that maximizes value and minimizes risk for the frame being analyzed. The optimization bots run simulations of purchasing activity, buyer risk and buyer return within the relevant portions of the Market Value Matrix™ framework using a non-linear, mixed integer optimization algorithm for each frame. Other optimization algorithms such as a genetic algorithm can be used to the same effect. Every optimization bot activated in this block contains the information shown in Table 8. TABLE 8 1. Unique ID number (based on date, hour, minute, second of creation) 2. Creation date (date, hour, minute, second) 3. Mapping information 4. Storage location 5. Buyer 6. Type: non-linear - mixed integer or genetic algorithm 7. Frame After the optimization bots are initialized, the bots activate in accordance with their preprogrammed instructions. After being activated, the bots determine the mix of features options that optimize the purchasing activity for each frame. The optimal mix is saved in the purchasing activity value table (151) in the application database (50) by frame before processing advances to a software block 310.

The software in block 310 checks the bot date table (149) and deactivates any sensitivity bots with creation dates before the current system date. The software in the block then uses the information that was previously retrieved (from the system settings table (140), metadata mapping table (141), the conversion rules table (142), the frame definition table (143), the purchasing system database table (144), the simulation program table (148)—if data from there is being used, the buyer's Value Matrix™ System table (150) and the external factor forecasts table (152) as required to initialize the sensitivity bots. Bots are independent components of the application that have specific tasks to perform. In the case of sensitivity bots, their primary task is to determine the sensitivity of the optimal purchasing activity mix to changes in external factor price, deliverable price, feature price and supplier by frame. The optimization bots run simulations of purchasing activity, buyer risk and buyer return within the relevant portions of the Market Value Matrix™ framework using a probabilistic simulation model such as a Monte Carlo Model for each frame. Other probabilistic simulation models can be used to the same effect. Every sensitivity bot activated in this block contains the information shown in Table 9. TABLE 9 1. Unique ID number (based on date, hour, minute, second of creation) 2. Creation date (date, hour, minute, second) 3. Mapping information 4. Storage location 5. Variable: external factor, feature or supplier 6. Buyer 7. Frame After the sensitivity bots are initialized, the bots activate in accordance with their preprogrammed instructions. After being activated, the bots determine how sensitive buyer risk, buyer return and the optimal mix of features are to changes in the purchasing activity variables. The results of this analysis are saved in the sensitivity analysis table (154) in the application database (50) by frame before processing advances to a software block 352.

The software in block 352 checks the system settings table (140) in the application database (50) to determine if the current optimization includes volume purchase discount analyses. If the purchasing activity that is being optimized includes volume purchase discounts, then processing advances to a software block 363. Alternatively, if the purchasing activity that is being optimized does not include volume purchase discounts, then processing advances to a software block 402.

The software in block 363 identifies the frames being analyzed using data from the frame definition table (143) and then uses the information from the metadata mapping table (141) to identify the purchasing activity data that needs to be retrieved from the purchasing system database table (144) for the frames being analyzed. After retrieving the required purchasing data which includes the deliverable requirements for the time period, the current orders, items (deliverables) by supplier (resources), item (deliverable) history by supplier, volume purchase discount schedules and pricing information, processing advances to a software block 364.

The software in block 364 checks the system settings table (140) in the application database (50) to determine if the current optimization is limited to a volume purchase discount analysis. If the purchasing activity that is being optimized is limited to a volume purchase discount analysis, then processing advances to a software block 370. Alternatively, if the purchasing activity that is being optimized is not limited to only volume purchase discount analyses, then processing advances to a software block 304.

The software in block 304 retrieves the metadata mapping table (141) data as required to identify and retrieve Market Value Matrix™ data via the Knowledge Layer regarding the specific value drivers that are linked to purchased resources, deliverables and features for the selected frames before processing advances to a software block 305. The software in block 305 retrieves the external factor prices for the purchasing activity being analyzed from the external factor forecast table (152) before processing advances to a software block 307.

The software in block 307 checks the system settings table (140) to determine if simulation program data is being used in the purchasing activity analysis. If simulation program data is being used, then processing advances to a software block 308. Alternatively, if simulation program data is not being used, then processing advances to a software block 309. The software in block 308 retrieves the feature, resource and deliverable data for the purchasing activity being analyzed from the simulation program table (148) before processing advances to a software block 370.

The software in block 370 checks the bot date table (149) and deactivates any optimization bots with creation dates before the current system date and uses the previously retrieved information (from the system settings table (140), metadata mapping table (141), the conversion rules table (142), the frame definition table (143), the purchasing system database table (144), optionally, the simulation program table (148), the buyer's Value Matrix™ System table (150) and the external factor forecast table (152). Bots are independent components of the application that have specific tasks to perform. In the case of optimization bots, their primary task is to determine the optimal mix of purchasing activity features by frame. The optimal mix is the mix that maximizes value and minimizes risk for the frame being analyzed. The optimization bots run simulations of purchasing activity, buyer risk and buyer return within the relevant portions of the Market Value Matrix™ framework using a genetic algorithm for each frame. Every optimization bot activated in this block contains the information shown in Table 10. TABLE 10 1. Unique ID number (based on date, hour, minute, second of creation) 2. Creation date (date, hour, minute, second) 3. Mapping information 4. Storage location 5. Buyer 6. Frame After the optimization bots are initialized, the bots activate in accordance with their preprogrammed instructions. After being activated, the bots determine the mix of features options that optimize the purchasing activity for each frame when volume purchase discounts are taken in to account. The optimal mix is saved in the purchasing activity value table (151) in the application database (50) by frame before processing advances to a software block 310.

The software in block 310 checks the bot date table (149) and deactivates any sensitivity bots with creation dates before the current system date. The software in the block then uses the information that was previously retrieved (from the system settings table (140), metadata mapping table (141), the conversion rules table (142), the frame definition table (143), the purchasing system database table (144), the simulation program table (148)—if data from there is being used, the buyer's Value Matrix™ System table (150) and the external factor forecasts table (152) as required to initialize the sensitivity bots. Bots are independent components of the application that have specific tasks to perform. In the case of sensitivity bots, their primary task is to determine the sensitivity of the optimal purchasing activity mix to changes in external factor price, deliverable price, feature price and supplier by frame. The optimization bots run simulations of purchasing activity, buyer risk and buyer return within the relevant portions of the Market Value Matrix™ framework using a probabilistic simulation model such as a Monte Carlo Model for each frame. Other probabilistic simulation models can be used to the same effect. Every sensitivity bot activated in this block contains the information shown in Table 11. TABLE 11 1. Unique ID number (based on date, hour, minute, second of creation) 2. Creation date (date, hour, minute, second) 3. Mapping information 4. Storage location 5. Variable: external factor, feature or supplier 6. Buyer 7. Frame After the sensitivity bots are initialized, the bots activate in accordance with their preprogrammed instructions. After being activated, the bots determine how sensitive buyer risk, buyer return and the optimal mix of features are to changes in the purchasing activity variables. The results of this analysis are saved in the sensitivity analysis table (154) in the application database (50) by frame before processing advances to a software block 402.

Reporting

The flow diagram in FIG. 7 details the processing that is completed by the portion of the application software (400) that performs special analyses, communicates the optimal mix to the purchasing system and the buyer's Value Matrix™ System before creating, displaying and optionally printing purchasing reports.

Processing in this portion of the application begins in software block 402. The software in block 402 retrieves information from the purchasing activity value table (151) as required to display the optimal mix of features and resources from the buyers frame. The optimal mix for other frames can also be displayed at this time. The software in block 402 then prompts the user (20) via the analysis definition window (905) to optionally edit the optimal mix that was displayed and/or to suggest other changes in the optimal mix. Any input regarding a change to the optimal mix is saved in the analysis definition table (146) before processing advances to a software block 403. The users input regarding changes in the optimal mix could also be forwarded to a simulation program at this point to determine if the user (20) specified changes had any material affect on the external factor consumption by the purchasing activity.

If the user (20) has specified changes to the optimal mix, then the software in block 403 completes an analysis of the impact of the changes from all relevant frames using the optimization process described previously for blocks 309 and 370. Other optimization algorithms can be used to the same effect. The software in block 403 also defines a probabilistic simulation model to analyze the proposed changes. The preferred embodiment of the probabilistic simulation model is a Markov Chain Monte Carlo model. However, other simulation models can be used with similar results. The model is defined using the information retrieved from the analysis definition table (146) and then iterated as required to ensure the convergence of the frequency distribution of the output variables. After the calculation has been completed, the software in block 403 saves the resulting information in the analysis definition table (146). After displaying the results of the optional change analysis using a report selection window (906), the user (20) is prompted to specify which set of features and feature options—the optimal mix or the mix defined by the user (20) should be passed on to purchasing system and the buyer's Value Matrix™ System. The mix selected for transmission is stored in the purchasing activity value table (151). After data storage is complete, the software in block 403 prompts the user (20) via the report selection window (906) to designate reports for creation, display and/or printing. One report the user (20) has the option of selecting at this point shows the value of each feature or resource to the purchasing activity and frame being analyzed. The report also summarizes the factors that led to the addition or exclusion of each feature and resource in the optimized purchasing activity mix. When the analysis is a comparison to a prior analysis, the report will clearly show the impact of changing one or more features or resources on the efficient frontier of the buyer as shown in FIG. 8. Other reports graphically display the sensitivity of the optimal mix to changes in the different feature and external factor prices for the different frames. After the user (20) has completed the review of displayed reports and the input regarding reports to print has been saved in the reports table (155) processing advances to a software block 404.

The software in block 404 retrieves the feature mix selected for transmission to the purchasing system database (30) and the buyer's Value Matrix™ System database from the purchasing activity value table (151) and transmits it via a network (25) before advancing to a software block 405. The transmission of information by the software in block 404 could use the information developed in the prior stages of processing to activate purchasing bots to implement the optimal purchasing mix and report back as appropriate regarding progress toward implementing the purchasing plan. In any event, the software in block 405 checks the reports tables (155) to determine if any reports have been designated for printing. If reports have been designated for printing, then processing advances to a block 406 where the software in the block prepares and sends the designated reports to the printer (118). After the reports have been sent to the printer (118), processing advances to a software block 409. Alternatively, if the software in block 405 determines that no additional reports have been designated for printing, then processing advances to block 409.

The software in block 409 checks the system settings table (140) to see if the purchasing activity optimization is being run in continuous mode. If it is being run in continuous mode, then processing returns to software block 204 and the processing described previously is repeated. Alternatively, if the processing is not being run in continuous mode, then processing advances to a software block 415 where processing stops.

Thus, the reader will see that the system and method described above transforms extracted transaction data and information into a specification of the optimal purchasing mix for an enterprise or multi-enterprise organization. The level of detail contained in the purchase activity specification enables the analysis and simulation of the impact of changes in the purchasing activity mix on the future value and risk of the enterprise or multi-enterprise organization that is the buyer.

While the above description contains many specificities, these should not be construed as limitations on the scope of the invention, but rather as an exemplification of one preferred embodiment thereof. Accordingly, the scope of the invention should be determined not by the embodiment illustrated, but by the appended claims and their legal equivalents. 

1. A purchasing method, comprising: obtaining purchasing feature data and context for an organization; defining a simulation model using said context and data; and using said model to determine the set of purchasing features that optimize one or more aspects of financial performance for said organization.
 2. The method of claim 1 where purchasing features encapsulate all the options the purchasing manager has for acquiring deliverables.
 3. The method of claim 2 where resources are a subset of features that comprise the different suppliers that can provide deliverables.
 4. The method of claim 1 where the organization is a single product, a group of products, a division, a company, a multi-company corporation, a value chain or a collaboration.
 5. The method of claim 1 where context includes information from the group consisting of administrative context, strategic business context and combinations thereof.
 6. The method of claim 5 where administrative context is defined by the existing delivery commitments, purchase requirements, inventory and purchases.
 7. The method of claim 5 where the strategic business context is defined by the organization segments of value, elements of value, risks and external factors that will be affected by the purchasing feature selection.
 8. The method of claim 7 where the organization segments of value are current operations, derivatives, investments, market sentiment, real options and combinations thereof.
 9. The method of claim 7 where the organization risks are from the group consisting of element variability risks, external factor variability risks, contingent liabilities, strategic risks, market volatility risks, event risks and combinations thereof.
 10. The method of claim 7 where the organization elements of value are from the group consisting of alliances, brands, channels, customers, customer relationships, employees, equipment, knowledge, intellectual property, investors, partnerships, processes, quality, vendors, vendor relationships, visitors and combinations thereof.
 11. The method of claim 7 where the external factors are from the group consisting of numerical indicators of conditions external to the organization, numerical indications of prices external to the organization, numerical indications of organization conditions compared to external expectations of organization condition, numerical indications of the organization performance compared to external expectations of organization performance and combinations thereof.
 12. The method of claim 7 where identifying the segments of value, elements of value, risks and external factors that will be affected by the purchasing feature selection further comprises: obtaining an xml schema for the organization market value matrix, and mapping purchasing features to the market value matrix using said schema.
 13. The method of claim 7 further comprising the use of data from a simulation system to define context.
 14. The method of claim 1 that optionally displays the impact of the purchasing the optimized feature set on the position of the organization relative to the efficient frontier.
 15. The method of claim 1 where the method further comprises displaying the organization value and the optimal set of features using a paper document or electronic display.
 16. The method of claim 1 where the simulation model is the organization market value matrix or a separate model.
 17. The method of claim 1 where the context data is obtained from a web service, operating system layer or operating system extension.
 18. The method of claim 1 where the one or more aspects of organization financial performance are selected from the group consisting of alliance risk, brand risk, channel risk, content risk, contingent liabilities, customer risk, customer relationship risk, current operation risk, derivative risk, employee risk, employee relationship risk, energy risk, enterprise risk, external factor risk, event risk, fraud risk, information technology risk, intellectual property risk, investment risk, knowledge risk, market sentiment risk, market risk, market volatility, organization risk, partnership risk, process risk, production equipment risk, product risk, real option risk, technology risk, total risk, vendor risk, vendor relationship risk, weather risk, alliance return, brand return, channel return, content return, contingent liabilities, customer return, customer relationship return, current operation return, derivative return, employee return, employee relationship return, enterprise return, external factor return, event return, information technology return, intellectual property return, investment return, knowledge return, market sentiment return, market return, market volatility, organization return, partnership return, process return, production equipment return, product return, real option return, technology return, total return, vendor return, vendor relationship return, alliance value, brand value, channel value, content value, contingent liabilities, customer value, customer relationship value, current operation value, derivative value, employee value, employee relationship value, enterprise value, external factor value, event value, information technology value, intellectual property value, investment value, knowledge value, market sentiment value, market value, market volatility, organization value, partnership value, process value, production equipment value, product value, real option value, technology value, vendor value, vendor relationship value and combinations thereof.
 19. The method of claim 1 where non-linear mixed integer optimization algorithms, multi criteria optimizations or genetic algorithms are used for determining the optimal set of features.
 20. The method of claim 1 where the volume purchase discounts are considered in the determination of the optimal set of features.
 21. The method of claim 20 where volume purchase discounts considered are item discounts, committed business volume discounts, as ordered business volume discounts and combinations thereof.
 22. A facet optimization method, comprising: obtaining context and feature data for a facet of organization performance; defining a simulation model using said context and data; and using said model to determine the set of features that optimize one or more aspects of financial performance for said organization.
 23. The method of claim 22 where features encapsulate all the different options the organization management has for managing the facet of organization performance.
 24. The method of claim 23 where features include any options for implementing a feature at a future date.
 25. The method of claim 22 where the organization is a single product, a group of products, a division, a company, a multi-company corporation, a value chain or a collaboration.
 26. The method of claim 22 where context includes information from the group consisting of administrative context, strategic business context and combinations thereof.
 27. The method of claim 26 where administrative context is defined by the commitments, requirements and status of the facet being optimized.
 28. The method of claim 26 where the strategic business context is defined by the organization segments of value, elements of value, risks and external factors that will be affected by the feature selection.
 29. The method of claim 28 where the organization segments of value are current operations, real options, derivatives, investments, market sentiment and combinations thereof.
 30. The method of claim 28 where the organization risks are selected from the group consisting of element variability risks, external factor variability risks, contingent liabilities, strategic risks, market volatility risks, event risks and combinations thereof.
 31. The method of claim 28 where the organization elements of value are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, equipment, knowledge, intellectual property, investors, partnerships, processes, quality, vendors, vendor relationships, visitors and combinations thereof.
 32. The method of claim 28 where the external factors are selected from the group consisting of numerical indicators of conditions external to the organization, numerical indications of prices external to the organization, numerical indications of organization conditions compared to external expectations of organization condition, numerical indications of the organization performance compared to external expectations of organization performance and combinations thereof.
 33. The method of claim 28 where identifying the segments of value, elements of value, risks and external factors that will be affected by the purchasing feature selection further comprises: obtaining an xml schema for the organization market value matrix, and mapping facet features to the market value matrix using said schema.
 34. The method of claim 22 that optionally displays the impact of the optimized feature set on moving the organization closer to or further from the efficient frontier.
 35. The method of claim 22 where the method further comprises displaying the organization value and the optimal set of features using a paper document or electronic display.
 36. The method of claim 22 where the facet of organization performance is selected from the group consisting of alliance management, brand management, channel management, content management, contingent liability management, customer management, customer relationship management, current operation management, derivative management, employee management, employee relationship management, energy management, enterprise risk management, external factor risk management, event risk management, fraud risk management, information technology management, intellectual property management, investment management, knowledge management, market sentiment management, market risk management, market volatility management, organization management, partnership management, process management, production equipment management, product management, project management, purchasing management, real option management, technology management, total risk management, vendor management, vendor relationship management, weather risk management and combinations thereof.
 37. The method of claim 22 where the context data is obtained from a web service, operating system layer or operating system extension.
 38. The method of claim 22 where the one or more aspects of organization financial performance are selected from the group consisting of alliance risk, brand risk, channel risk, content risk, contingent liabilities, customer risk, customer relationship risk, current operation risk, derivative risk, employee risk, employee relationship risk, energy risk, enterprise risk, external factor risk, event risk, fraud risk, information technology risk, intellectual property risk, investment risk, knowledge risk, market sentiment risk, market risk, market volatility, organization risk, partnership risk, process risk, production equipment risk, product risk, project risk, real option risk, technology risk, total risk, vendor risk, vendor relationship risk, weather risk, alliance return, brand return, channel return, content return, contingent liabilities, customer return, customer relationship return, current operation return, derivative return, employee return, employee relationship return, enterprise return, external factor return, event return, information technology return, intellectual property return, investment return, knowledge return, market sentiment return, market return, market volatility, organization return, partnership return, process return, production equipment return, product return, real option return, technology return, total return, vendor return, vendor relationship return, alliance value, brand value, channel value, content value, contingent liabilities, customer value, customer relationship value, current operation value, derivative value, employee value, employee relationship value, enterprise value, external factor value, event value, information technology value, intellectual property value, investment value, knowledge value, market sentiment value, market value, market volatility, organization value, partnership value, process value, production equipment value, product value, project value, real option value, technology value, vendor value, vendor relationship value and combinations thereof.
 39. The method of claim 22 where non-linear mixed integer optimization algorithms, multi-criteria optimizations or genetic algorithms are used for determining the optimal mix of features.
 40. The method of claim 22 further comprising the use of simulation system data in defining context.
 41. A computer readable medium having sequences of instructions stored therein, which when executed cause the processors in a plurality of computers that have been connected via a network to perform a facet optimization method, comprising: obtaining context and feature data for a facet of organization performance; defining a simulation model using said context and data; and using said model to determine the set of features that optimize one or more aspects of financial performance for said organization.
 42. The computer readable medium of claim 41 where features encapsulate all the different options the organization management has for managing the facet of organization performance.
 43. The computer readable medium of claim 42 where features include any options for implementing a feature at a future date.
 44. The computer readable medium of claim 41 where the organization is a single product, a group of products, a division, a company, a multi-company corporation, a value chain or a collaboration.
 45. The computer readable medium of claim 41 where context includes information from the group consisting of administrative context, strategic business context and combinations thereof.
 46. The computer readable medium of claim 45 where administrative context is defined by the commitments, requirements and status of the facet being optimized.
 47. The computer readable medium of claim 45 where the strategic business context is defined by the organization segments of value, elements of value, risks and external factors that will be affected by the feature selection.
 48. The computer readable medium of claim 47 where the organization segments of value are selected from the group consisting of current operations, real options, derivatives, investments, market sentiment and combinations thereof.
 49. The computer readable medium of claim 47 where the risks are selected from the group consisting of element variability risks, external factor variability risks, contingent liabilities, strategic risks, market volatility risks, event risks and combinations thereof.
 50. The computer readable medium of claim 47 where the elements of value are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, equipment, knowledge, intellectual property, investors, partnerships, processes, quality, vendors, vendor relationships, visitors and combinations thereof.
 51. The computer readable medium of claim 47 where the external factors are selected from the group consisting of numerical indicators of conditions external to the organization, numerical indications of prices external to the organization, numerical indications of organization conditions compared to external expectations of organization condition, numerical indications of the organization performance compared to external expectations of organization performance and combinations thereof.
 52. The computer readable medium of claim 47 where identifying the segments of value, elements of value, risks and external factors that will be affected by the purchasing feature selection further comprises: obtaining an xml schema for the organization market value matrix, and mapping facet features to the market value matrix using said schema.
 53. The computer readable medium of claim 41 that optionally displays the impact of the optimized feature set on moving the organization closer to or further from the efficient frontier.
 54. The computer readable medium of claim 41 where the method further comprises displaying the organization value and the optimal set of features using a paper document or electronic display.
 55. The computer readable medium of claim 41 where the facet of organization performance is selected from the group consisting, of alliance management, brand management, channel management, content management, contingent liability management, customer management, customer relationship management, current operation management, derivative management, employee management, employee relationship management, energy management, enterprise risk management, external factor risk management, event risk management, fraud risk management, information technology management, intellectual property management, investment management, knowledge management, market sentiment management, market risk management, market volatility management, organization management, partnership management, process management, production equipment management, product management, project management, purchasing management, real option management, technology management, total risk management, vendor management, vendor relationship management, weather risk management and combinations thereof.
 56. The computer readable medium of claim 41 where the context data is obtained from a web service, operating system layer or operating system extension.
 57. The computer readable medium of claim 41 where the one or more aspects of organization financial performance are selected from the group consisting of alliance risk, brand risk, channel risk, content risk, contingent liabilities, customer risk, customer relationship risk, current operation risk, derivative risk, employee risk, employee relationship risk, energy risk, enterprise risk, external factor risk, event risk, fraud risk, information technology risk, intellectual property risk, investment risk, knowledge risk, market sentiment risk, market risk, market volatility, organization risk, partnership risk, process risk, production equipment risk, product risk, project risk, real option risk, technology risk, total risk, vendor risk, vendor relationship risk, weather risk, alliance return, brand return, channel return, content return, contingent liabilities, customer return, customer relationship return, current operation return, derivative return, employee return, employee relationship return, enterprise return, external factor return, event return, information technology return, intellectual property return, investment return, knowledge return, market sentiment return, market return, market volatility, organization return, partnership return, process return, production equipment return, product return, real option return, technology return, total return, vendor return, vendor relationship return, alliance value, brand value, channel value, content value, contingent liabilities, customer value, customer relationship value, current operation value, derivative value, employee value, employee relationship value, enterprise value, external factor value, event value, information technology value, intellectual property value, investment value, knowledge value, market sentiment value, market value, market volatility, organization value, partnership value, process value, production equipment value, product value, project value, real option value, technology value, vendor value, vendor relationship value and combinations thereof.
 58. The computer readable medium of claim 41 where non-linear mixed integer optimization algorithms, multi-criteria optimizations or genetic algorithms are used for determining the optimal mix of features.
 59. A facet optimization system, comprising: computers connected by a network each with a processor having circuitry to execute instructions; a storage device available to each processor with sequences of instructions stored therein, which when executed cause the processors to: obtain context and feature data for a facet of organization performance; define a simulation model using said context and data; and use said model to determine the set of features that optimize one or more aspects of financial performance for said organization.
 60. The system of claim 59 where features encapsulate all the different options the organization management has for managing the facet of organization performance.
 61. The system of claim 59 where the organization is a single product, a group of products, a division, a company, a multi-company corporation, a value chain or a collaboration.
 62. The system of claim 59 where context includes information from the group consisting of administrative context, strategic business context and combinations thereof.
 63. The system of claim 59 that optionally displays the impact of the optimized feature set on moving the organization closer to or further from the efficient frontier.
 64. The system of claim 59 where the method further comprises displaying the organization value and the optimal set of features using a paper document or electronic display.
 65. The system of claim 59 where the context data is obtained from a web service, operating system layer or operating system extension.
 66. The system of claim 59 where the one or more aspects of organization financial performance are selected from the group consisting of alliance risk, brand risk, channel risk, content risk, contingent liabilities, customer risk, customer relationship risk, current operation risk, derivative risk, employee risk, employee relationship risk, energy risk, enterprise risk, external factor risk, event risk, fraud risk, information technology risk, intellectual property risk, investment risk, knowledge risk, market sentiment risk, market risk, market volatility, organization risk, partnership risk, process risk, production equipment risk, product risk, project risk, real option risk, technology risk, total risk, vendor risk, vendor relationship risk, weather risk, alliance return, brand return, channel return, content return, contingent liabilities, customer return, customer relationship return, current operation return, derivative return, employee return, employee relationship return, enterprise return, external factor return, event return, information technology return, intellectual property return, investment return, knowledge return, market sentiment return, market return, market volatility, organization return, partnership return, process return, production equipment return, product return, real option return, technology return, total return, vendor return, vendor relationship return, alliance value, brand value, channel value, content value, contingent liabilities, customer value, customer relationship value, current operation value, derivative value, employee value, employee relationship value, enterprise value, external factor value, event value, information technology value, intellectual property value, investment value, knowledge value, market sentiment value, market value, market volatility, organization value, partnership value, process value, production equipment value, product value, project value, real option value, technology value, vendor value, vendor relationship value and combinations thereof.
 67. The system of claim 59 where the facet of organization performance is selected from the group consisting of alliance management, brand management, channel management, content management, contingent liability management, customer management, customer relationship management, current operation management, derivative management, employee management, employee relationship management, energy management, enterprise risk management, external factor risk management, event risk management, fraud risk management, information technology management, intellectual property management, investment management, knowledge management, market sentiment management, market risk management, market volatility management, organization management, partnership management, process management, production equipment management, product management, project management, purchasing management, real option management, technology management, total risk management, vendor management, vendor relationship management, weather risk management and combinations thereof. 